Developing Effective Strategies and Performance Metrics for Automatic Target Recognition

2004 ◽  
Author(s):  
M. S. Alam ◽  
A. A. S. Awwal ◽  
K. Iftekharuddin
Keyword(s):  
Performance Metrics ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Effective Strategies ◽  
And Performance

Performance Evaluation for an Automatic Target Recognition System with the Application of Sugeno Fuzzy Integration

2015 ◽  
Vol 738-739 ◽  
pp. 311-315
Author(s):  
Yan Peng Li ◽  
Yu Liang Qin ◽  
Hong Qiang Wang
Keyword(s):  
Performance Evaluation ◽  
Evaluation Method ◽  
Performance Metrics ◽  
Target Recognition ◽  
Evaluation Model ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
Performance Evaluation Model ◽  
Novel Method ◽  
Performance Evaluation Method

Automatic Target Recognition (ATR) system is widely utilized in engineering. However, the performance evaluation method for an ATR system is limited. This paper resolves the problem based on the theory of Sugeno fuzzy integration. The performance metrics are firstly measured. Then, a performance evaluation model is developed. Simulation result shows that, compared to the existing technologies, the novel method can offer more objective performance conclusions for an ATR system.

Feature and Extractor Evaluation Concepts for Automatic Target Recognition (ATR)

1995 ◽  
Author(s):  
Timothy D. Ross ◽  
Lori A. Westerkamp ◽  
David A. Gadd ◽  
Robert B. Kotz
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition

Application of Decision Analysis to Automatic Target Recognition Programmatic Decisions

2002 ◽  
Author(s):  
William K. Klimack ◽  
Christopher B. Bassham ◽  
Kenneth W. Bauer ◽  
Jr
Keyword(s):  
Decision Analysis ◽  
Target Recognition ◽  
Automatic Target Recognition

scholarly journals SAR Target Recognition via Meta-Learning and Amortized Variational Inference

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5966
Author(s):  
Ke Wang ◽  
Gong Zhang
Keyword(s):  
Target Recognition ◽  
Probability Distributions ◽  
Automatic Target Recognition ◽  
Variational Inference ◽  
Training Data ◽  
Superior Performance ◽  
Small Data ◽  
Meta Learning ◽  
Radar Automatic Target Recognition ◽  
Global Parameters

The challenge of small data has emerged in synthetic aperture radar automatic target recognition (SAR-ATR) problems. Most SAR-ATR methods are data-driven and require a lot of training data that are expensive to collect. To address this challenge, we propose a recognition model that incorporates meta-learning and amortized variational inference (AVI). Specifically, the model consists of global parameters and task-specific parameters. The global parameters, trained by meta-learning, construct a common feature extractor shared between all recognition tasks. The task-specific parameters, modeled by probability distributions, can adapt to new tasks with a small amount of training data. To reduce the computation and storage cost, the task-specific parameters are inferred by AVI implemented with set-to-set functions. Extensive experiments were conducted on a real SAR dataset to evaluate the effectiveness of the model. The results of the proposed approach compared with those of the latest SAR-ATR methods show the superior performance of our model, especially on recognition tasks with limited data.

Advanced Automatic Target Recognition (ATR) with Infrared (IR) Sensors

2021 ◽  
Author(s):  
Hai- Wen Chen ◽  
Neal Gross ◽  
Ravi Kapadia ◽  
Joseph Cheah ◽  
Mo Gharbieh
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Ir Sensors
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